Errors In Judgment Research Articles

50 Years of advance care planning: what do we call success?

Advance care planning (ACP) is promoted as beneficial practice internationally. This article critically examines different ways of understanding and measuring success in ACP. It has been 50years since Luis Kutner first published his original idea of the Living Will, which was thought to be a contract between health carers and patients to provide for instructions about treatment choices in cases of mental incapacity. Its purpose was to extend a patient's right to autonomy and protect health carers from charges of wrong-doing. Yet, it can be doubtful whether different types of ACP achieve these goals rather than aiming at secondary gains. My discussion suggests that the current promotion of ACP is not always engaging critically with the original ACP intentions and may even pursue notions of success that may run contrary to respecting autonomy. The risk of this may especially be the case when high participation rates are taken as indicators of success for institutional ACP programs. I further suggest that Kutner's two original aims of protecting patient autonomy and preventing charges of wrong-doing are near impossible to achieve in conjunction, because their simultaneous pursuit fails to acknowledge that patients and carers have opposing needs for reassurance about possible judgment errors. I conclude that the most realistic idea of success of modern ACP is an acknowledgement of the importance of ongoing dialogue about what constitutes appropriate care and a diversity of aims rather than any kind of advance, contractual insurance in the face of controversy.

Oversampling of minority categories drives misperceptions of group compositions

The ability to estimate proportions informs our immediate impressions of social environments (e.g., of the diversity of races or genders within a crowded room). This study examines how the distribution of attention during brief glances shapes estimates of group gender proportions. Performance-wise, subjects exhibit a canonical pattern of judgment errors: small proportions are overestimated while large values are underestimated. Subjects' eye movements at sub-second timescales reveal that these biases follow from a tendency to visually oversample members of the gender minority. Rates of oversampling dovetail with average levels of error magnitudes, response variability, and response times. Visual biases are thus associated with the inherent difficulty in estimating particular proportions. All results are replicated at a within-subjects level with non-human ensembles using natural scene stimuli; the observed attentional patterns and judgment biases are thus not exclusively guided by face-specific visual properties. Our results reveal the biased distribution of attention underlying typical judgment errors of group proportions.

‘From the Wells of Disappointment’: The Curious Case of the International Law of Democracy and the Politics of International Legal Scholarship

Abstract It is a common impression shared by many international lawyers today that the brief ‘turn to democracy’ that occurred in some segments of international legal scholarship in the early to mid-1990s was, on the whole, little more than a detour of overly excitable imagination – not exactly a complete error of judgement or an outright frivolity, but certainly a lapse of conceptual clarity and professional rigour. Whatever changes may have occurred within the broader international legal system, the argument goes, they certainly did not amount to a ‘democratic revolution’, and any claims to the contrary were and are simply baseless. The kind of fundamental reorganization of the international legal system that was forecasted by scholars like Thomas Franck and Anne-Marie Slaughter never took place, and the main lesson one should learn from this whole episode is that international legal scholars should not give in to their utopian reflexes as quickly and as readily as the ‘pro-democracy enthusiasts’ did, but should rather exercise analytical restraint and professional judgement and attend much more carefully to matters of legal logic and technical legal reasoning. This, in a nutshell, is the received wisdom about the history behind international law’s ‘turn to democracy’, and the aim of this article is essentially to challenge it – in part by uncovering the latent theoretical fudging behind it, in part by exploring the general narrative structure that supports this received wisdom and the latter’s relationship to the broader ideology of international legal anti-utopianism.

10 STARTUPS TO WATCH 2021: Early-stage companies advance precision medicine by applying AI, clinical genomics, and other new technologies

10 STARTUPS TO WATCH 2021: Early-stage companies advance precision medicine by applying AI, clinical genomics, and other new technologies

Systematic Early Urinary Catheter Removal Integrated in the Full Enhanced Recovery After Surgery (ERAS) Protocol After Laparoscopic Mid to Lower Rectal Cancer Excision: A Feasibility Study.

PurposeEnhanced Recovery After Surgery (ERAS) programs advocate early urinary catheter removal after rectal cancer surgery; however, the optimal duration remains unclear. This study assessed the feasibility of the early urinary catheter removal protocol after rectal cancer surgery within an ERAS pathway and identified predictive factors for failure of this strategy.MethodsBetween March 2017 and October 2018, all unselected and consecutive patients who underwent rectal cancer resection and benefited from our ERAS program were included. Urinary complications (infection and retention) were prospectively recorded. Success was defined as catheter removal on postoperative day (POD) 3 without urinary complications.ResultsOf 135 patients (male, 63.7%; neoadjuvant chemoradiation, 57.0%; urology history, 17.8%), 120 had early urinary catheter removal with no complications (success rate, 88.9%), 8 did not have urinary catheter removal on POD 3 due to clinical judgment or prescription error, 5 experienced a urinary tract infection, and 2 had acute urinary retention. Obesity (odds ratio [OR], 0.16; P = 0.003), American Society of Anesthesiologists physical status classification > II (OR, 0.28; P = 0.048), antiaggregant platelet medication (OR, 0.12; P < 0.001), absence of anastomosis (OR, 0.1; P = 0.003), and prolonged operative time (OR, 0.21; P = 0.020) were predictive factors for failure. Conversely, optimal compliance with the ERAS program (OR, 7.68; P < 0.001), postoperative nonsteroidal anti-inflammatory drug use (OR, 21.71; P < 0.001), and balanced intravenous fluid therapy (OR, 7.87; P = 0.001) were associated with increased strategy success.ConclusionWithdrawal of the urinary catheter on POD 3 was successfully achieved after laparoscopic rectal resection and can be safely implemented in the ERAS program.

Processing of Ordinal Information in Math-Anxious Individuals

This study aimed to investigate whether the ordinal judgments of high math-anxious (HMA) and low math-anxious (LMA) individuals differ. Two groups of 20 participants with extreme scores on the Shortened Mathematics Anxiety Rating Scale (sMARS) had to decide whether a triplet of numbers was presented in ascending order. Triplets could contain one-digit or two-digit numbers and be formed by consecutive numbers (counting condition), numbers with a constant distance of two or three (balanced) or numbers with variable distances between them (neutral). All these triplets were also presented unordered: sequence order in these trials could be broken at the second (D2) or third (D3) number. A reverse distance effect (worse performance for ordered balanced than for counting trials) of equal size was found in both anxiety groups. However, HMA participants made more judgment errors than their LMA peers when they judged one-digit counting ordered triplets. This effect was related to worse performance of HMA individuals on a symmetry span test and might be related to group differences on working memory. Importantly, HMAs were less accurate than LMA participants at rejecting unordered D2 sequences. This result is interpreted in terms of worse cognitive flexibility in HMA individuals.

Development of a brief, generic, modular resource-use measure (ModRUM): cognitive interviews with patients

BackgroundSelf-report resource-use measures (RUMs) are often used to collect healthcare use data from participants in healthcare studies. However, RUMs are typically adapted from existing measures on a study-by-study basis, resulting in a lack of standardisation which limits comparability across studies. Psychometric testing of RUMs is rarely conducted. This paper reports on cognitive interviews with patients to test the content validity and acceptability of a new RUM (ModRUM). ModRUM is a brief, generic RUM with a core module on healthcare use and questions/modules to increase depth and breadth.MethodsA purposeful sampling strategy with maximum variation was used to recruit patients from primary care to participate in “think-aloud” interviews with retrospective probing. Participants verbalised their thought processes as they completed ModRUM, which allowed errors (issues with completion) to be identified. The interviewer asked follow-up and probing questions to investigate errors, clarity and acceptability.Interviews were audio-recorded and transcribed verbatim. Research team members independently scored transcripts to identify errors in comprehension, recall, judgement and response. Members met to agree on final scores. Interview transcripts were analysed qualitatively using techniques of constant comparison, to identify common themes and ideas for improvement. Data collection and analysis were performed concurrently and in rounds.ResultsTwenty participants were interviewed between December 2019 and March 2020. Interviews were conducted in three rounds, with revisions made iteratively and in response to interview findings. Seven participants completed the core module and 13 completed the core module plus depth questions. Of 71 issues, 28 were in comprehension, 14 in retrieval, 10 in judgement, 18 in response and 1 uncategorised. Most issues (21 issues by 2 participants) were due to participants including family healthcare use. Other issues included using incorrect recall periods (5 issues) and overlooking questions leading to missing responses (9 issues). Common participant suggestions included highlighting important details and providing additional definition or examples for some terms. The length, content and layout were acceptable to most participants.ConclusionsA generic RUM is needed to increase study comparability. RUM development requires thorough testing to demonstrate and enhance validity. Cognitive interviewing has demonstrated the acceptability and content validity of ModRUM.

Evaluating the sustainable mining contractor selection problems: An imprecise last aggregation preference selection index method

The increasing complexity surrounding decision-making situations has made it inevitable for practitioners to apply ideas from a group of experts or decision makers (DMs) instead of individuals. In a large proportion of recent studies, not enough attention has been paid to considering uncertainty in practical ways. In this paper, a hesitant fuzzy preference selection index (HFPSI) method is proposed based on a new soft computing approach with risk preferences of DMs to deal with imprecise multi-criteria decision-making problems. Meanwhile, qualitative assessing criteria are considered in the process of the proposed method to help the DMs by providing suitable expressions of membership degrees for an element under a set. Moreover, the best alternative is selected based on considering the concepts of preference relation and hesitant fuzzy sets, simultaneously. Therefore, DMs' weights are determined according to the proposed hesitant fuzzy compromise solution technique to prevent judgment errors. Moreover, the proposed method has been extended based on the last aggregation method by aggregating the DMs' opinions during the last stage to avoid data loss. In this respect, a real case study about the mining contractor selection problem is provided to represent the effectiveness and efficiency of the proposed HFPSI method in practice. Then, a comparative analysis is performed to show the feasibility of the presented approach. Finally, sensitivity analysis is carried out to show the effect of considering the DMs' weights and last aggregation approach in a dispersion of the alternatives’ ranking values.

FUNDAMENTAL &amp; TECHNICAL ANAYSIS OF CRUDE OIL PRICES

The human mind is not as good at processing large amounts of information as we might like. Psychologists have shown that human beings are only able to juggle small numbers of related and often conflicting pieces of information without making judgment errors. As a result, individuals faced with the vast amounts of information available to support investment decisions often find themselves swamped by the enormity of the task; unable to see the wood from the trees. Technical analysis is a field of financial markets research that works to address the above problem by focusing on a single, commonly available, data source that reflects all known information and activity relating to all monetary securities- Price history. Technical analysts argue that as markets are efficient, prices reflect all known information and that they move over time as participants react to new information and changing needs.&#x0D; As a result, the technical analysis of these price changes can provide real insight into the market dynamics and be used to develop trade strategies that exhibit superior risk/reward characteristics. While technical analysis approaches have developed significantly over the past few decades, some techniques are far more ancient. While their real origins are anonymous, Japanese candlestick charts have been recorded as being employed in the rice markets as far back as the 1600s. What is particularly interesting is that various of these ancient approaches continue to provide highly effective trading signals when applied to modern markets and securities.&#x0D; Crude oil price volatility is in the midst of the largest business risk that oil and gas companies face. This is followed by unstable policy regime, managing costs and risks emerging from technological advancements. The high levels and rapid fluctuations of petroleum prices have become a great concern to individual consumers, firms, policy makers and society. Technical Analysis is the forecasting of future financial price movements based on an examination of past price movements. Like weather forecasting, technical analysis does not result in absolute predictions about the future. Instead, technical analysis can help investors anticipate what is "likely" to happen to prices over time. Technical analysis uses a wide variety of charts that show price over time. Hence, to mitigate the negative impacts of price volatility and to predict about the future price movement of crude oil and natural gas we can use technical analysis.&#x0D; Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting price trends. The term “market action” includes the three principal source of action available to the technician-price, volume and open interest. This research paper highlights fundamental factor which affects the Brent price and analysed the factor which are highly correlated with Brent price and on the basis of the results forecasted the Brent price for next five years. Fundamental analysis of Brent oil, price pattern &amp; movement of crude oil has also been carried out using candlestick technical tool.

Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Transplant

Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve patient outcomes. In the future, integrating all of this information with other patient history data will likely have to rely on artificial intelligence (AI). AI agents can help augment transplant decisionmaking by discovering patterns and making predictions for specific patients that are not covered in the literature or in ways that are impossible for humans to anticipate by integrating vast amounts of data (e.g. trending across numerous biomarkers). Similar to other clinical decision support systems, AI may help overcome human biases or judgment errors. However, AI is not widely utilized in transplant to date. In this rapid review, we survey the methods employed in recent research in transplant-related AI applications and identify concerns related to implementing these tools. We identify three key challenges (bias/accuracy, clinical decision process/AI explainability, AI acceptability criteria) holding back AI in transplant. We also identify steps that can be taken in the near term to help advance meaningful use of AI in transplant (forming a Transplant AI Team at each center, establishing clinical and ethical acceptability criteria, and incorporating AI into the Shared Decision Making Model).

Design and implementation of an integrated pipeline security system with optmized scheduling

&lt;p&gt;Offshore equipment and facilities are some of the most capital intensive and strategically located assets that require millions to billions of dollars to setup. Thus, it is important to put in place a stout security and surveillance system to ultimately protect these assets from vandalisation and theft. Monitoring of intruders/ unfamiliar objects within and outside the offshore platform requires a round-the-clock observation through provided security personnel who are stationed so as to quickly respond to any threat within a safe distance in the event of any physical intrusion but this measure is fraught with some challenges. Some of these challenges include fatigue, error in human judgement, and the limited vision of humans to mention but a few. To remedy these weaknesses and ensure a robust protection, the design and implementation/ setting up of a surveillance system to detect and track real time security concerns with a much wider and effective coverage area to enable the control unit make a well informed decision appropriately is an imperative. The system presented in this paper is designed to operate in harsh (sea waves, and fog) and unprotected (extreme heat, cold precipitation) environment.&lt;/p&gt;

Data Sensitivity Measurement and Classification Model of Power IOT based on Information Entropy and BP Neural Network

For the problems of privacy data protection caused by massive data sharing in the construction of power Internet of things, a data sensitivity measurement and classification model based on information entropy and BP neural network is proposed. Firstly, a recognition matching algorithm is proposed to identify the sensitive level of attributes in the dataset, and the information entropy is used to determine the weight of attributes sensitivity level, so as to calculate the sensitivity measurement value of records in the dataset; finally, the BP neural network is used to output the data classification results. The experimental results show that the model can achieve accurate measurement and classification of data, with low incorrect judgment rate and error rate.

Misinformed and unaware? Metacognition and the influence of inaccurate information.

The current study investigated the role of metacognition with respect to the consequences of exposures to inaccurate information. Previous work has consistently demonstrated that exposures to inaccuracies can confuse people and even encourage reliance on the falsehoods. We specifically examined whether people are aware of their likelihood of being influenced by inaccurate information, and whether engaging in metacognitive reflection is effective at reducing this influence. In three experiments, participants read a story containing false assertions about the world. In Experiment 1, we compared participants' estimated resistance to inaccurate information against the degree to which their subsequent judgments actually reflected an influence of previously read inaccuracies. Participants were generally unaware of their susceptibility to inaccurate information, demonstrated by a lack of calibration between estimated and actual resistance. Their judgments consistently revealed an influence of previously read inaccuracies. In Experiment 2, we applied a metacognitive reflection task intended to encourage evaluation while reading. Participants who completed this task made fewer judgment errors after having read inaccurate statements than did participants who did not engage in reflection. Experiment 3 replicated these effects with a larger sample, and showed benefits of reflection for calibrations between people's estimated resistance and their actual performance. The accumulated findings highlight the importance of metacognitive considerations for understanding and addressing oft-reported, problematic effects of exposures to inaccuracies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The Art of Dance from the Perspective of Artificial Intelligence

With the continuous improvement of artificial intelligence application level, computer data processing technology has gradually become the main and classic auxiliary technology application method in the daily work and life of the majority of society members. This article is based on artificial intelligence technology, using computer animation technology, combining artificial intelligence, computer music and graphics disciplines, to show the musical image in the virtual dance of characters, involving the analysis and recognition of musical elements, the establishment of character models, and motion control And other issues. This paper explores the Hidden Markov Model (IIMM, HiddenMarkovModel) model characteristics and algorithm training methods, and proposes an improved HMM training method model algorithm to identify common dance movement models and the control and synthesis of basic dance gestures. This system uses animation as a virtual dance character to show more dance artistic movements for the issues related to the display and scoring of dance movements. Based on the dance movement recognition algorithm and other related algorithms (such as score and error judgment algorithm, error reduction algorithm, etc.), this paper constructs a set of dance movement technical feature statistics and dance movement training. This system realizes the technical statistics and dance training of dance competitions. Two major functions. The above research solves the technical problems of software and hardware engineering from data collection to algorithm programming, and provides a functional platform for intelligent analysis and auxiliary training of dance art, which can be promoted and applied to more professional dancers and dance teachers, thereby accumulating a large amount of data and refining Produce more technical index parameters in line with the rules of dance art, and finally form a set of professional products for dance art intelligence analysis and auxiliary training.

Modelling road traffic Noise under heterogeneous traffic conditions using the graph-theoretic approach.

A traffic noise system involves several subsystems like road traffic subsystem, human subsystem, environment subsystem, traffic network subsystem, and urban prosperity subsystem. The study’s main aim was to develop road traffic noise models using a graph theory approach involving the parameters related to road traffic subsystem. The road traffic subsystem variables selected for the modeling purposes included vehicular speed, traffic volume, carriageway width, number of heavy vehicles, and number of honking events. The interaction of the selected variables considered in the form of permanent noise function is given in the matrix form. Eigenvalues and corresponding eigenvectors are calculated for removing any human judgmental error. The permanent noise function matrix was then updated using the eigenvectors, which was ultimately utilized for obtaining the permanent noise index. Data regarding the selected variables were collected for three months, and the noise parameters included in the study were equivalent noise level (Leq,1h), maximum noise level (L10,1h), and background noise level (L90,1h). A logarithmic transformation was applied to the permanent noise index and linear regression models were developed for Leq,1h , L90,1h , and L10,1h respectively. The models were validated using the data collected from the same locations for nine months. The models were found to provide satisfactory results, although the results were somewhat overestimated. The method can prove beneficial for estimating future noise levels, given the expected changes in values for the independent variables considered in the study.

Probabilistic social learning improves the public's judgments of news veracity.

The digital spread of misinformation is one of the leading threats to democracy, public health, and the global economy. Popular strategies for mitigating misinformation include crowdsourcing, machine learning, and media literacy programs that require social media users to classify news in binary terms as either true or false. However, research on peer influence suggests that framing decisions in binary terms can amplify judgment errors and limit social learning, whereas framing decisions in probabilistic terms can reliably improve judgments. In this preregistered experiment, we compare online peer networks that collaboratively evaluated the veracity of news by communicating either binary or probabilistic judgments. Exchanging probabilistic estimates of news veracity substantially improved individual and group judgments, with the effect of eliminating polarization in news evaluation. By contrast, exchanging binary classifications reduced social learning and maintained polarization. The benefits of probabilistic social learning are robust to participants’ education, gender, race, income, religion, and partisanship.

Application of Machine Learning to Predict And Diagnose Diabetes

This article uses linear regression in machine learning and LightGBM algorithm for data mining, and compares the linear regression and LightGBM algorithm’s ability of fast running speed and high accuracy under the same data conditions. The least squares method is used to make error judgments to realize the rapid and accurate prediction and judgment of the probability of diabetes in a large amount of data. At the same time, the importance and correlation of the variables are compared between the variables. Among them, it is found that Body Mass Index (BMI) has the greatest impact on diabetes, and the factors affecting BMI are weight and height. Height and age can be input into system, automatically determining the BMI value, and providing doctors with a basis for judgment.

First price sealed-bid auction of the grey game improvement model via double-parameter Bayesian estimation

PurposeThe improved classical model makes it possible that the evaluation strategy has an optimal tendency, which reveals the purpose of this paper is to facilitate the first price sealed-bid auction more in line with the actual situation. To be more specific, there are several merits in the improvement process. On the one hand, the bid-winning probability can be improved for the bidder; on the other hand, the real market value of the subject matter can be more clearly recognized for the employer.Design/methodology/approachBayesian estimation and grey system theory are referenced in this paper, with the use of double-parameter estimation, little historical data and expert experience. Specific implementation steps are as follows: first of all, using the double-parameter Bayesian estimation to correct the actual valuation of the bid matter v, then introducing the threat factor grey number R in the auction model, giving the improving of the optimal grey quotation and grey expectation utility under the two-party game and finally taking the aerospace component procurement as an example, simulating the bidding process of the bidding parties to arrive at the optimal bid strategy.FindingsThe improved model shows that the optimal strategy will change with the threat factor rather than a fixed value. When the threat factor grey number R follows [0.4, 0.6], the optimal quotation strategy will appear, which means quotation is higher than 50% of the bid matter's valuation.Practical implicationsThe improved model proposed in this paper can strengthen the cost control in the Chinese commercial space process and optimize the pricing strategy for the final launch.Originality/valueThe modified model changes the habit that the bidder's valuation of the bid subject to mainly come from experience and to prompt the model for making full use of little historical data on the foundation of the former. It can reduce the subjective judgment error in the game results; finally, the practical cases are simulated in MATLAB at the same time, and the simulation effect is good, so we can get some more realistic conclusions on this basis.

Don't Just Judge the Spelling! The Influence of Spelling on Assessing Second-Language Student Essays

When judging subject-specific aspects of students’ texts, teachers should assess various characteristics, e.g., spelling and content, independently of one another since these characteristics are indicators of different skills. Independent judgments enable teachers to adapt their classroom instruction according to students’ skills. It is still unclear how well teachers meet this challenge and which intervention could be helpful to them. In Study 1, N = 51 pre-service teachers assessed four authentic English as a Second Language (ESL) essays with different overall text qualities and different qualities of spelling using holistic and analytic rating scales. Results showed a negative influence of the experimentally manipulated spelling errors on the judgment of almost all textual characteristics. In Study 2, an experimental prompt was used to reduce this judgment error. Participants who were made aware of the judgment error caused by spelling errors formed their judgments in a less biased way, indicating a reduction of bias. The determinants of the observed effects and their practical implications are discussed.

The role of corporate governance failure in the Grenfell Tower fire.

The capacity for crisis perception and to foresee risk is central to project management where responsibility for safety is a central component informing decision making. This article examines the absence of this capacity as a cause and consequence of corporate governance failure. The example of the Grenfell Tower fire is used to illustrate the multiple inflection points within governance structures where relatively minor errors of judgement or inattention to detail individually or collectively undermine their shared purpose. The consequences of minor deficiencies include the risk of compromising the governance framework, misaligning stakeholder objectives, normalising miscommunication, mismanagement and neglect, institutionalising discrimination and, ultimately, eroding the protection measures required to prevent catastrophic events.